This document is the summary of the Introduction to R workshop.
All correspondence related to this document should be addressed to:
Omid Ghasemi (Macquarie University, Sydney, NSW, 2109, AUSTRALIA)
Email: omidreza.ghasemi@hdr.mq.edu.auThe aim of the study is to test if simple arguments are more effective in belief revision than more complex arguments. To that end, we present participants with an imaginary scenario (two alien creatures on a planet) and a theory (one creature is predator and the other one is prey) and we ask them to rate the likelihood truth of the theory based on a simple fact (We adapted this method from Gregg et al.,2017; see the original study here). Then, in a between-subject manipulation, participants will be presented with either 6 simple arguments (Modus Ponens conditionals) or 6 more complex arguments (Modus Tollens conditionals), and they will be asked to rate the likelihood truth of the initial theory on 7 stages.
The first stage is the base rating stage. The next three stages include supportive arguments of the theory and the last three arguments include disproving arguments of the theory. We hypothesized that the group with simple arguments shows better persuasion (as it reflects in higher ratings for the supportive arguments) and better dissuasion (as it reflects in lower ratings for the opposing arguments).
In the last part of the study, participants will be asked to answer several cognitive capacity/style measures including thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales. We hypothesized that cognitive ability, cognitive style, and open-mindedness are positive predictors of persuasion and dissuasion. These associations should be more pronounced for participants in the group with complex arguments because the ability and willingness to engage in deliberative thinking may favor participants to assess the underlying logical structure of those arguments. However, for participants in the simple group, the logical structure of arguments is more evident, so participants with lower ability can still assess the logical status of those arguments.
Thus, our hypotheses for this experiment are as follows:
Participants in the group with simple arguments have higher ratings for supportive arguments (They are more easily persuaded than those in the group with complex arguments).
Participants in the group with simple arguments have lower ratings for opposing arguments (They are more easily dissuaded than those in the group with complex arguments).
There are significant associations between thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales with both persuasion and dissuasion indexes in each group and in the entire sample. The relationship between these measures should be stronger, although not significantly, for participants in the group with complex arguments.
First, we need to design the experiment. For this experiment, we use online platforms for data collection. There are several options such as Gorilla, JSpsych, Qualtrics, psychoJS (pavlovia), etc. Since we do not need any reaction time data, we simply use Qualtrics. For an overview of different lab-based and online platforms, see here.
Next, we need to decide on the number of participants (sample size). For this study, we do not sue power analysis since we cannot access more than 120 participants. However, it is highly suggested calculate sample size using power estimation. You can find some nice tutorials on how to do that here, here, and here.
After we created the experiment and decided on the sample size, the next step is to preresigter the study. However, it would be better to do a pilot with 4 or 5 participants, clean all the data, do the desired analysis, and then pre-register the analysis and those codes. You can find the preregistration form for the current study here.
Finally, we need to restructure our project in a tidy folder with different sub-folders. Having a clean and tidy folder structure can save us! There are different formats of folder structure (for example, see here and here), but for now, we use the following structure:
# load libraries
library(tidyverse)
library(here)
library(janitor)
library(broom)
library(afex)
library(emmeans)
library(knitr)
library(kableExtra)
library(ggsci)
library(patchwork)
library(skimr)
# install.packages("devtools")
# devtools::install_github("easystats/correlation")
library("correlation")
options(scipen=999) # turn off scientific notations
options(contrasts = c('contr.sum','contr.poly')) # set the contrast sum globally
options(knitr.kable.NA = '')
Artwork by Allison Horst: https://github.com/allisonhorst/stats-illustrations
R can be used as a calculator. For mathematical purposes, be careful of the order in which R executes the commands.
10 + 10
## [1] 20
4 ^ 2
## [1] 16
(250 / 500) * 100
## [1] 50
R is a bit flexible with spacing (but no spacing in the name of variables and words)
10+10
## [1] 20
10 + 10
## [1] 20
R can sometimes tell that you’re not finished yet
10 +
How to create a variable? Variable assignment using <- and =. Note that R is case sensitive for everything
pay <- 250
month = 12
pay * month
## [1] 3000
salary <- pay * month
Few points in naming variables and vectors: use short, informative words, keep same method (e.g., not using capital words, use only _ or . ).
Function is a set of statements combined together to perform a specific task. When we use a block of code repeatedly, we can convert it to a function. To write a function, first, you need to define it:
my_multiplier <- function(a,b){
result = a * b
return (result)
}
This code do nothing. To get a result, you need to call it:
my_multiplier (a=2, b=4)
## [1] 8
# or: my_multiplier (2, 4)
We can set a default value for our arguments:
my_multiplier2 <- function(a,b=4){
result = a * b
return (result)
}
my_multiplier2 (a=2)
## [1] 8
# or: my_multiplier (2)
# or: my_multiplier (2, 6)
Fortunately, you do not need to write everything from scratch. R has lots of built-in functions that you can use:
round(54.6787)
## [1] 55
round(54.5787, digits = 2)
## [1] 54.58
Use ? before the function name to get some help. For example, ?round. You will see many functions in the rest of the workshop.
function class() is used to show what is the type of a variable.
TRUE, FALSE can be abbreviated as T, F. They has to be capital, ‘true’ is not a logical data:class(TRUE)
## [1] "logical"
class(F)
## [1] "logical"
class(2)
## [1] "numeric"
class(13.46)
## [1] "numeric"
class("ha ha ha ha")
## [1] "character"
class("56.6")
## [1] "character"
class("TRUE")
## [1] "character"
Can we change the type of data in a variable? Yes, you need to use the function as.---()
as.numeric(TRUE)
## [1] 1
as.character(4)
## [1] "4"
as.numeric("4.5")
## [1] 4.5
as.numeric("Hello")
## Warning: NAs introduced by coercion
## [1] NA
Vector: when there are more than one number or letter stored. Use the combine function c() for that.
sale <- c(1, 2, 3,4, 5, 6, 7, 8, 9, 10) # also sale <- c(1:10)
sale <- c(1:10)
sale * sale
## [1] 1 4 9 16 25 36 49 64 81 100
Subsetting a vector:
days <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
days[2]
## [1] "Sunday"
days[-2]
## [1] "Saturday" "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"
days[c(2, 3, 4)]
## [1] "Sunday" "Monday" "Tuesday"
Create a vector named my_vector with numbers from 0 to 1000 in it:
my_vector <- (0:1000)
mean(my_vector)
## [1] 500
median(my_vector)
## [1] 500
min(my_vector)
## [1] 0
range(my_vector)
## [1] 0 1000
class(my_vector)
## [1] "integer"
sum(my_vector)
## [1] 500500
sd(my_vector)
## [1] 289.1081
List: allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other list.
my_list = list(sale, 1, 3, 4:7, "HELLO", "hello", FALSE)
my_list
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4 5 6 7
##
## [[5]]
## [1] "HELLO"
##
## [[6]]
## [1] "hello"
##
## [[7]]
## [1] FALSE
Factor: Factors store the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character. For example, variable gender with “male” and “female” entries:
gender <- c("male", "male", "male", " female", "female", "female")
gender <- factor(gender)
R now treats gender as a nominal (categorical) variable: 1=female, 2=male internally (alphabetically).
summary(gender)
## female female male
## 1 2 3
Question: why when we ran the above function i.e. summary(), it showed three and not two levels of the data? Hint: run ‘gender’.
gender
## [1] male male male female female female
## Levels: female female male
So, be careful of spaces!
Create a gender factor with 30 male and 40 females (Hint: use the rep() function):
gender <- c(rep("male",30), rep("female", 40))
gender <- factor(gender)
gender
## [1] male male male male male male male male male male
## [11] male male male male male male male male male male
## [21] male male male male male male male male male male
## [31] female female female female female female female female female female
## [41] female female female female female female female female female female
## [51] female female female female female female female female female female
## [61] female female female female female female female female female female
## Levels: female male
There are two types of categorical variables: nominal and ordinal. How to create ordered factors (when the variable is nominal and values can be ordered)? We should add two additional arguments to the factor() function: ordered = TRUE, and levels = c("level1", "level2"). For example, we have a vector that shows participants’ education level.
edu<-c(3,2,3,4,1,2,2,3,4)
education<-factor(edu, ordered = TRUE)
levels(education) <- c("Primary school","high school","College","Uni graduated")
education
## [1] College high school College Uni graduated
## [5] Primary school high school high school College
## [9] Uni graduated
## Levels: Primary school < high school < College < Uni graduated
We have a factor with patient and control values. Here, the first level is control and the second level is patient. Change the order of levels, so patient would be the first level:
health_status <- factor(c(rep('patient',5),rep('control',5)))
health_status
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: control patient
health_status_reordered <- factor(health_status, levels = c('patient','control'))
health_status_reordered
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: patient control
Finally, can you relabel both levels to uppercase characters? (Hint: check ?factor)
health_status_relabeled <- factor(health_status, levels = c('patient','control'), labels = c('Patient','Control'))
health_status_relabeled
## [1] Patient Patient Patient Patient Patient Control Control Control
## [9] Control Control
## Levels: Patient Control
Matrices: All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. It can be created using a vector input to the matrix function.
my_matrix = matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, ncol = 3)
my_matrix
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Data frames: (two-dimensional objects) can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type. Let’s create a dataframe:
id <- 1:200
group <- c(rep("Psychotherapy", 100), rep("Medication", 100))
response <- c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5))
my_dataframe <-data.frame(Patient = id,
Treatment = group,
Response = response)
We also could have done the below
my_dataframe <-data.frame(Patient = c(1:200),
Treatment = c(rep("Psychotherapy", 100), rep("Medication", 100)),
Response = c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5)))
In large data sets, the function head() enables you to show the first observations of a data frames. Similarly, the function tail() prints out the last observations in your data set.
head(my_dataframe)
tail(my_dataframe)
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 31.46027 |
| 2 | Psychotherapy | 25.80691 |
| 3 | Psychotherapy | 23.90582 |
| 4 | Psychotherapy | 35.24277 |
| 5 | Psychotherapy | 36.12894 |
| 6 | Psychotherapy | 28.88709 |
| Patient | Treatment | Response | |
|---|---|---|---|
| 195 | 195 | Medication | 29.98481 |
| 196 | 196 | Medication | 17.56606 |
| 197 | 197 | Medication | 27.15201 |
| 198 | 198 | Medication | 25.47396 |
| 199 | 199 | Medication | 25.41576 |
| 200 | 200 | Medication | 26.85049 |
Similar to vectors and matrices, brackets [] are used to selects data from rows and columns in data.frames:
my_dataframe[35, 3]
## [1] 24.10207
How can we get all columns, but only for the first 10 participants?
my_dataframe[1:10, ]
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 31.46027 |
| 2 | Psychotherapy | 25.80691 |
| 3 | Psychotherapy | 23.90582 |
| 4 | Psychotherapy | 35.24277 |
| 5 | Psychotherapy | 36.12894 |
| 6 | Psychotherapy | 28.88709 |
| 7 | Psychotherapy | 28.13632 |
| 8 | Psychotherapy | 26.74151 |
| 9 | Psychotherapy | 28.07995 |
| 10 | Psychotherapy | 25.66144 |
How to get only the Response column for all participants?
my_dataframe[ , 3]
## [1] 31.46027 25.80691 23.90582 35.24277 36.12894 28.88709 28.13632
## [8] 26.74151 28.07995 25.66144 32.43043 32.90031 28.22010 20.49055
## [15] 21.60086 29.88247 28.37732 28.33941 32.99115 35.60892 28.20174
## [22] 31.02228 37.67104 26.73629 23.17044 29.62874 27.47495 29.36538
## [29] 36.66284 37.21628 33.22692 28.58497 29.40086 30.05996 24.10207
## [36] 37.68405 34.11073 35.26914 35.77968 23.37928 29.54027 26.18844
## [43] 38.75336 29.88913 35.19795 32.72946 29.27546 31.04248 33.29102
## [50] 18.01849 29.91320 30.06106 30.93769 29.09398 31.92382 31.01322
## [57] 26.27603 28.00506 29.75021 32.14493 24.69866 31.15726 22.59687
## [64] 33.51499 20.87766 24.72480 28.88959 23.23752 22.79437 25.97967
## [71] 40.90827 30.38728 38.08255 37.16809 25.73616 25.32099 28.77482
## [78] 34.21075 33.13974 29.55064 23.93372 30.95174 28.30632 37.92416
## [85] 27.14505 19.64008 34.20583 44.02146 29.18339 35.25485 39.78779
## [92] 32.02400 33.92133 31.74780 22.73999 31.02149 24.51421 28.80851
## [99] 37.00935 39.01414 24.46241 33.60924 26.14423 19.96034 24.18702
## [106] 25.42803 26.15863 29.76837 25.03232 25.53612 26.98493 23.44438
## [113] 14.90449 28.02894 24.57431 22.30127 34.10020 20.91610 33.11796
## [120] 37.12811 22.89906 31.94684 27.52520 22.31867 13.92906 19.25865
## [127] 30.37195 24.67580 26.37599 24.65865 28.30528 23.66096 17.49513
## [134] 23.04172 15.84645 18.23439 26.63121 29.81582 32.55382 25.66231
## [141] 28.64869 23.77102 32.00628 25.16474 32.48781 25.98615 22.57022
## [148] 19.36977 28.48540 26.29446 21.81714 24.45360 17.95169 23.09270
## [155] 21.38275 27.08464 18.66920 27.60698 27.70646 24.17864 22.87044
## [162] 24.70419 19.10310 21.52611 25.94191 26.26336 27.21419 26.85767
## [169] 17.14670 24.41177 31.19198 24.97077 24.81557 21.71635 26.72816
## [176] 24.91654 28.13910 21.55515 19.29760 26.87387 23.21848 26.19222
## [183] 25.00059 33.82250 27.32508 35.48514 32.61955 32.35353 10.76799
## [190] 15.55515 23.96873 23.10777 26.09092 21.18117 29.98481 17.56606
## [197] 27.15201 25.47396 25.41576 26.85049
Another easier way for selecting particular items is using their names that is more helpful than number of the rows in large data sets:
my_dataframe[ , "Response"]
# OR:
my_dataframe$Response